In order to develop a search technique that can be applied to larger search spaces and different variational quantum problems, we introduce an algorithm for QAS problems based on combinatorial multi-armed bandit (CMAB) model as well as Monte-Carlo Tree Search (MCTS). In order to explore extremely large search spaces compared to previous work in the literature, the working of our strategy is underpinned by a reward scheme which dictates the choices of the quantum operations at each step of the algorithm with the naïve assumption [1]}. This enabled our strategy to work on larger systems, more than 7 qubits, whereas the existing examples [2]}, [3]}, [4]}, [2]}, [6]}, [7]} are restricted to typically 3 or 4 qubits, with the largest being 6 qubits. To demonstrate the working of our method, we showed its application to a variety of problems including encoding the logic states for the [[4,2,2]] quantum error detection code, solving the ground energy problem for different molecules as well as linear systems of equations, and searching the ansätz for solving optimisations problems. Our work confirms that the automated quantum architecture search based on the MCTS+CMAB approach exhibits great versatility and scalability, and therefore should provide an efficient solution and new insights to the problems of designing variational quantum circuits.
